Infrastructure Engineering - Theses
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ItemTernary spatial relations for error detection in map databases( 2020)The quality of data in spatial databases greatly affects the performance of location-based applications that rely on maps such as emergency dispatch, land and property ownership registration, and delivery services. The negative effects of such dirty map data may range from minor inconveniences to life-threatening events. Data cleaning usually consists of two steps - error detection and error rectification. Data cleaning is a demanding and lengthy process that requires manual interventions of data experts, in particular where for complex situations involving the consistency of relationships between multiple objects. This thesis presents computational methods developed to automate the detection of errors in map databases and ease the demand for human resources in error detection. These methods are intrinsic, ie., depend only on data being analysed, without the need for a reference dataset. Two models for ternary spatial relations were developed to enable the analyses not possible with existing binary spatial relations. First, the Refined Topological relations model for Line objects (RTL) examines whether the core line object is connected to its surrounding objects on both or only one of its ends. This distinction is particularly important in networks where connectedness determines the function of the object. Second, the Ray Intersection Model (RIM) casts rays between two peripheral objects and uses the intersection sets between these rays and the core object to model its relation to peripheral objects. This provides a basis for reasoning about the core object being between peripheral objects. Both models have been computationally implemented and demonstrated on error detection tasks in OpenStreetMap. The case studies on data for the State of Victoria, Australia demonstrate that the methods developed in this research are effectively detecting errors that could so far not be automatically identified. This research contributes to automated spatial data cleaning and quality assurance, including reducing experts' workload by effectively identifying potential errors.
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ItemA framework for micro level assessment and 3D visualisation of flood damage to a building( 2016)Flood Damage Assessment (FDA) is the key component of the flood risk management process. By highlighting the potential consequences of floods, FDA allows for an evidence-based risk management by employing optimal risk reduction measures in the community. FDA is generally performed in three main scales namely Macro, Meso and Micro. For assessing the potential flood damages at different levels, various categories of vulnerable elements (e.g. roads, people, buildings, etc.) are accounted for. Among these elements, buildings are the most notable and are considered in nearly all the current FDA methods due to their significance to the economy. In addition, with increasing risks of floods due to the climate change effects, the attention to improve the flood resilience of buildings is increasing. This leads to the need for a more profound understanding of the fluid-structure interactions and assessing the potential damages and risks to the building from floods in the early design and planning stages. Amongst the FDA methods, in contrast to the aggregated land use as the inputs of Macro and Meso models, only those Micro level assessments can provide separate analysis for the buildings. However, the current micro-level FDA models cannot account for the distinct characteristics of each building and its unique behaviour against floods. Therefore, they are associated with high uncertainties. Additionally, the current models only account for either damage from the flood loads or those as the result of floodwater contacting with water-sensitive components. This leads to incomplete outputs and further increase in the uncertainty of the results. Moreover, the existing FDA models mostly focus on the quantitative assessment of damages and do not communicate the mode/type of damage to important decision makers (e.g. designers and engineers). This inhibits the optimal selection of measures for reducing the risk to buildings. While the need of larger-scale applications are well satisfied by the existing FDA methods, the highlighted limitations hinder the use of these methods to effectively assess the damage and risks in situations where individual buildings are the focus of the analysis. To address the aforementioned limitations of the previous models, in this multidisciplinary research by adopting the Design Science Research Methodology an FDA framework was developed. This framework allows for a detailed micro-level assessment and 3D visualisation of flood damage to a building and according to its unique characteristics and behaviour against floods. The proposed processes in the framework were designed in detail according to the well-established theories in a number of related domains. Moreover, by developing a new BIM-GIS integration method, rich inputs about a building and flood parameters could be provided for the framework to effectively overcome the data input limitations of the current FDA models. The framework was realised by development of a prototype system and on the basis of the proposed guidelines. The dual evaluation of the framework using the internal validity checking as well as the use of a case study underlined the feasibility of implementation and the effective application of the framework for solving real-world problems. The benefits of the proposed framework for assessment and communication of flood damage at the building level was also highlighted to a variety of users. The framework can be employed as a complementary approach to the current FDA models for improving the resilience of the community towards floods and their adverse impacts.
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ItemAutomatic spatial metadata updating and enrichment( 2013)Spatial information is necessary to make sound decisions at the local, regional and global levels. As a result, the amount of spatial datasets being created and exchanged between organisations or people over the networked environment is dramatically increasing. As more data and information is produced, it becomes more vital to manage and locate such resources. The role in which spatial metadata, as a summary document providing content, quality, type, creation, distribution and spatial information about a dataset, plays in the management and location of these resources has been widely acknowledged. However, the current approaches cannot effectively manage metadata creation, updating, and improvement for an ever-growing amount of data created and shared in the Spatial Data Infrastructures (SDIs) and data sharing platforms. Among the available approaches, the manual approach has been considered monotonous, time-consuming, and a labour-intensive task by organisations. Also, the existing semi-automatic metadata approaches mainly focus on specific dataset formats to extract a limited number of metadata values (e.g. bounding box). Moreover, metadata is commonly collected and created in a separate process from the spatial data lifecycle, which requires the metadata author or responsible party to put extra effort into gathering necessary data for metadata creation and updating. In addition, dataset creation and editing are detached from metadata creation and editing procedures, necessitating diligent updating practices involving at a minimum, two separate applications. Metadata and related spatial data are often stored and maintained separately using a detached data model that results in avoiding automatic and simultaneous metadata updating when a dataset is modified. The spatial data end users are also disconnected from the metadata creation and improvement process. Accordingly, this research investigated a framework and associated approaches and tools to facilitate and automate the spatial metadata creation, updating and enrichment processes. This framework consists of three complementary approaches namely ‘lifecycle-centric spatial metadata creation’, ‘automatic spatial metadata updating (synchronisation)’, and ‘automatic spatial metadata enrichment’ and a newly integrated data model for storing and exchanging spatial dataset and metadata jointly. The lifecycle-centric spatial metadata creation approach aimed to create metadata in conjunction with the spatial data lifecycle steps. The automatic spatial metadata updating (synchronisation) approach was founded on a GML-based integrated data model to update metadata affected by the dataset modification concurrent with any change to the dataset, regardless of dataset format. The automatic spatial metadata enrichment approach was also design-rooted in Web 2.0 features (tagging and folksonomy) to improve the content of spatial metadata keyword element through monitoring the end users’ interaction with the data discovery and retrieval process. The proposed integrated data model and automatic spatial metadata updating and enrichment approaches were successfully implemented and tested via prototype systems. The prototype systems then were assessed against a number of requirements identified for the spatial metadata management and automation and effectively responded to those requirements.
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ItemDevelopment of a knowledge base for low-volume roads using a geographic information system( 2011)Currently each State Jurisdiction holds significant expenditure and road section activity data which are in varying formats and classifications. A Knowledge Management technique can extract differing data sets across multi-criteria in order to build up comprehensive data sets. Potentially this sound knowledge base can make more precise analysis and strategic decisions for low-volume roads. Geographic Information System (GIS) has been used in this research as the platform of this knowledge base due to its powerful data integration ability. One GIS software (TransCAD) has been chosen to combine all the existing data and also to estimate the traffic data as the available data is insufficient on building up such a knowledge base. Using traffic assignment and matrix estimation techniques, traffic volume data can be estimated from limited data source to produce a more comprehensive database. Nevertheless, not all the traffic assignment techniques have been tested and matrix estimation result cannot be validated until real data are acquired. It provides an approach when developing such a knowledge base, and with more input, results can be improved and a sound knowledge base is ready to be built.
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ItemDetecting change in an environment with wireless sensor networks( 2010)This thesis is motivated by a new observation tool, called wireless sensor network (WSN). WSN, as any other observation method, must cope with spatial, temporal, and thematic granularities of observations. This thesis investigates how WSNs can efficiently detect different types of changes of spatial phenomena, or objects. Two types of changes, i.e., gradual and abrupt spatial changes, are distinguished based on the quantity of change in particular, WSN inherent granularities. A new spatiotemporal data model is proposed for the representation of dynamic spatial objects in WSNs. An algorithm is then designed for WSNs to detect both gradual and abrupt spatial changes with different spatial, temporal, and thematic granularities. The efficiency of the algorithm is proven by qualitative and quantitative evaluation in WSN simulations. This thesis also proposes a new network structure, called multi-granularity sensor network, for different granularities of observations.